Background Directory UMM :Journals:Journal of Operations Management:Vol18.Issue3.Apr2000:

ciency measurement of U.S. airports, set the stage for this study. The primary objective of this paper is to determine those characteristics that impact the operations of major U.S. airports and to obtain re- sults that will aid operations managers and communi- ties in improving their airports by benchmarking their airports against similar airports. Data are gath- ered directly from the airports and also from the Ž . Airports Council International ACI and are input to a series of models that evaluate the relative effi- ciency of sets of airports. The modeling techniques, which are based on a set of mathematical program- ming formulations defined as data envelopment anal- Ž . ysis DEA , are briefly described in Section 3, with additional detail presented in Appendix A. As well as showing the applicability of the vari- ous DEA models, results are investigated to deter- mine characteristics that may impact airport opera- Ž tional efficiency e.g., whether an airport is a hub to w a major air carrier, is in a snowbelt regions with x more than 10 in. of snow per year , and part of a w x. multiple airport system MAS . This paper incorpo- rates many versions of DEA for efficiency analysis; a brief comparison and analysis of these techniques and their results are also included, followed by a summary and discussion of the results and future research potential.

2. Background

Airports are critical, dominant forces in a commu- Ž nity’s economic development e.g., Dallas–Fort . Ž . Worth and Atlanta . Inamete 1993 states that since 1970, airports have redrawn the economic map of the U.S. Locating airports in communities to further their economic development has been exacerbated by the deregulation of the airline industry, which has allowed airlines to expand services and pressured airports to provide additional services to the airlines’ customers. Ž . Inamete 1993 states that airport operational effi- ciencies may be improved through internal and ex- ternal measures. Government policies are strong ex- ternal measures, while communication and close management of operational, technical, and manage- rial functions are clear internal measures. The rela- tionship between the key elements of airport man- agement and policy milieu also impacts airport oper- ations. Improvements and evaluations of airport opera- tional efficiencies have not been well researched by the literature, perhaps due to the relatively recent introduction of operational improvement paradigms such as total quality management and business pro- cess reengineering. External forces for operational improvement include efforts by regulatory organiza- tions such as the Federal Aviation Administration Ž . FAA , which itself has experienced government reengineering. We review literature focusing on related effi- ciency studies, as well as issues and external charac- teristics that may impact airport operational effi- ciency. Airport operations managers may benchmark their airports’ performances against those of compa- rable airports on input and output measures used in these studies and consider these factors to interpret their findings more accurately. 2.1. Analysis of airport operations Few studies have focused on the productivity and efficiency of major U.S. airports. Productivity can be Ž . defined as a general measure of a ratio of output s Ž . to input s . The focus on productivity measurement in this industry typically has been on organizations that use the services of airports and on general Ž transportation infrastructure e.g., Schefczyk, 1993; . Truitt and Haynes, 1994; Windle and Dresner, 1995 . Efficiency, which is defined in more detail in the discussion on DEA models in Appendix A, considers Ž the relative productivity of a set of units in this case, . airports . An efficient unit is said to lie on the efficient frontier of a set of units. The deregulation of the airline industry has put pressure on airports to be more competitive and productive because airlines choose airports that are Ž . more cost effective. Ashford 1994, p. 59 makes a cogent argument for the improved management of airports in a deregulated airline environment: ‘‘Facil- ities which are efficient, inexpensive, cost effective and offering a high level of service to airlines and passengers can expect higher passenger flows and consequently increased revenues and increased prof- itability. In a deregulated climate, such a facility could expect to attract air carrier operation in an environment where the airline is free to move its base of operations.’’ Simply put, an air carrier’s willingness to remain at an airport may be deter- mined by that airport’s efficiency. Airport opera- tions, and the role of airport operations managers, have critical strategic implications for an airport’s long run viability. Table 1 Listing of airports and characteristic categorizations Ž . Airport Location Airport name Major carrier Airport system Snowbelt 10 in. abbreviation hub category snow annually ATL Atlanta, GA Hartsfield Intl. Yes SAS No BUF Buffalo, NY Greater Buffalo Intl. No SAS Yes BWI Maryland BaltimorerWashington Intl. Yes MAS Yes CLE Cleveland, OH Cleveland–Hopkins Intl. Yes MAS Yes CLT North Carolina CharlotterDouglas Intl. Yes SAS No DAL Dallas, TX Love Field Yes MAS No a DAY Dayton, OH Dayton Intl. Yes SAS Yes DEN Denver, CO Denver Intl. Yes SAS Yes DFW Irving, TX Dallas–Fort Worth Intl. Yes MAS No FLL Florida Fort Lauderdale Exec. No MAS No GEG Spokane, WA Spokane No SAS Yes GRR Grand Rapids, MI Kent County Intl. No SAS Yes HNL Hawaii Honolulu No SAS No HOU Houston, TX Houston Intercontinental Yes MAS No IAD Maryland Dulles Intl. Yes MAS Yes IAH Houston, TX William P. Hobby Yes MAS No IND Indiana Indianapolis Intl. No SAS Yes JAX Florida Jacksonville Intl. No SAS No JFK New York, NY John F. Kennedy Yes MAS Yes LAS Las Vegas, NV McCarran Intl. Yes SAS No LAX Los Angeles, CA Los Angeles Intl. Yes MAS No LGA New York, NY La Guardia No MAS Yes MCI Kansas City, MO Kansas City No SAS Yes MCO Orlando, FL Orlando Intl. Yes SAS No MEM Memphis, TN Memphis Shelby County Yes SAS No MIA Miami, FL Miami Intl. Yes MAS No MKE Milwaukee, WI General Mitchell No SAS Yes MSP Minnesota Minneapolis–St. Paul Yes SAS Yes MSY Louisiana New Orleans Intl. No SAS No OAK California Oakland Intl. Yes MAS No ONT Los Angeles, CA Ontario Intl. Yes MAS No PDX Portland, OR Portland Intl. No SAS No PHX Phoenix, AZ Sky Harbor Intl. Yes SAS No PIT Pittsburgh, PA Pittsburgh Intl. Yes SAS Yes RNO Reno, NV RenorTahoe Intl. No SAS Yes SDF Louisville, KY Louisville Intl. Yes SAS Yes SEA Seattle, WA Seattle–Tacoma Intl. Yes SAS No SFO California San Francisco Intl. No MAS No b SJC California San Jose Municipal Yes MAS No SLC Utah Salt Lake City Intl. Yes SAS Yes SMF California Sacramento Metro No SAS No SNA Los Angeles, CA John Wayne No MAS No STL St. Louis, MO Lambert Yes SAS Yes TPA Tampa, FL Tampa Intl. No SAS No a Before 1992. b Before 1993. Airports seek funding from the FAA’s airport Ž . improvement program AIP , a program critical for airport operations because its spending represents a Ž . substantial portion 20–25 of the national airport Ž . system’s capital costs see DeLuca et al., 1995 . Similar to most other governmental programs, it is undergoing evaluation and reengineering. The areas of change of the FAA airports reengineering project include national planning, master agreement devel- opment, resource reallocation, performance measure- ments, information technology development, and outreach programs. Three of these areas focus on the performance measures of airports related to any AIP funding and operations. The first, national planning, includes the development and publication of a report that measures actual and temporal improvements in airport system performance. In the second, the real- location of FAA resources will depend heavily on performance measures after AIP completion at an airport and on airport resource utilization. The third major area of change, performance measures, ad- dresses an important need for the national planning Ž . process because 1 it is the basis for determining Ž . national airport system performance, and 2 it guides the creation of a prioritized inventory of airport improvement projects. Six performance measurement areas have been defined for airport development systems: infrastructure, environment, accessibility, Ž . capacity, and investment FAA, 1997, p. 26 . The FAA adds that the priority system will be adjusted depending on the measurement of system perfor- mance as determined by performance measures Ž . FAA, 1996, 1997 such as efficiency evaluations. In addition to the consideration of airport effi- ciency, the results of this study are used to evaluate some characteristics of airports and their relation- ships to the efficiency measures, which will help the FAA and communities to compare airports. It will also show airport management that certain external characteristics may result in varying performances and that to benchmark their performances meaning- fully, they need to consider these characteristics. 2.2. Airline hub location and relation to airport operational efficiency Ž Most of the major air carriers except Southwest . Airlines have a transportation system based on the hub and spoke network model. The location of a hub at an airport greatly increases many airport output measures, including revenue and passenger flow. Thus, we expect that the operational efficiency of hub airports will be greater because either they are major air carrier hubs or air carriers chose these airports as hubs because they are more efficient. This study will not discern the causation, but will focus on the relationship between operational efficiency and whether an airport has an air carrier hub. The limited empirical and theoretical research on hub airport characteristics has focused on ‘‘fortress hub’’ and hub duopoloyrmonopoly relationships with air- Ž port fare prices see Borenstein, 1989; Windle and . Dresner, 1993 . The effects on airport operations of whether an airport is a hub have not been considered by any research. A hub airport is defined as one that is officially a Ž hub for a major airline or carriers in the U.S. except . Southwest Airlines . The major private airlines and carriers include: Alaska Airlines, American Airlines, America West, Continental Airlines, Delta Airlines, Federal Express, Northwest Airlines, Southwest Air- lines, Trans World Airlines, United Parcel Services, United Airlines, and US Airways. For Southwest Airlines, airports where over 25 of passenger traf- fic is from the Southwest are considered hubs. The Ž air carriers themselves American Airlines, United . Parcel Services, Federal Express and Air Transport World, a major trade journal, provide data sources. The categorization of airports as hubrnonhub is shown in Table 1. Only those airports that responded to this study are included in Table 1. We thus have our first proposition. Proposition 1. Airports that are hubs for major air carriers are more efficient than those that are not hubs. 2.3. Multiple airport systems Ž . Hansen and Weidner 1995 have studied the characteristics of a variety of MAS and the potential and need for additional MAS. The relative efficiency scores from the DEA execution in our data also may be used to evaluate the differences between MAS Ž . airports and those of single airport systems SAS . Ž . According to Hansen and Weidner p. 9 , an MAS is two or more airports with scheduled passenger en- planements, and which satisfy both of the following criteria. Ø Each airport is included in the same community Ž . by the FAA or within 50 km 30 miles of the primary airport of an FAA designated ‘large hub’ community, or each airport is in the same Metropoli- tan Statistical Area or Consolidated MSA. 1 Ž . Ø The Herfindahl concentration index HCI for the airports is less than 0.95. MAS airports, typically, have more passenger en- planements due to their locations in densely popu- lated areas, which may increase their efficiency scores. In addition, airports within MAS compete with each other, further emphasizing the need for efficiency. Hansen and Weidner imply that competi- tion in MAS provides a foundation for privatization of airports. SAS airports also may be efficient be- cause they represent the major passenger enplane- ment traffic in a geographical region and have rela- tively higher outputs, which the DEA models utilize. Categorization of MASrSAS airports is identified in Table 1. MAS airports are identified according to Ž . Hansen and Weidner 1995 . Proposition 2. Airports in Multiple Airport Systems are more efficient than those in Single Airport Sys- tems. 2.4. Geographical considerations and relationship to airport operational efficiency While providing the data, some respondents ex- pressed concern about the fact that geographic loca- tion, especially snowbelt vs. nonsnowbelt, may strongly influence relative airport productivity and efficiency. A brief analysis of these categories is presented. Airport categorizations of snowbelt or nonsnowbelt locations are shown in Table 1. We now state our third proposition. 1 The HCI is a measure of the degree to which passenger activity is concentrated at a single airport within the region. It is calculated as the sum of the squared traffic shares of each airport in an MAS. For an SAS the HCI is equal to 1. Proposition 3. Airports that are not in snowbelts are more efficient than those in snowbelts.

3. Methodology